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"en": "The dataset includes understory spectral reflectance factors at wavelengths of 350–2500 nm, fractional cover data of understory, forest inventory data, and data on the light availability at forest floor (diffuse non-interceptance, DIFN) and tree canopy structure (effective plant area index, PAIe) in hemiboreal and temperate forests in Europe. The data were collected near peak growing seasons 2019–2020 in situ in total of 31 forest stands in a hemiboreal study site in Estonia (13 stands: 58°17’N, 27°19’E), and temperate mountain (8 stands: 49°30′N, 18°32′E) and floodplain (10 stands: 48°41′N, 16°57′E) sites in the Czech Republic.\r\n\r\nThe first spread sheet contains descriptions of the variables, second spread sheet the stand characteristics, and third the spectral data.\r\n\r\nWe thank Daniel Schraik, Bijay Karki, Titta Majasalmi, Petr Lukeš, Lucie Homolová, Mait Lang, Jan Pisek, Mihkel Kaha, and Lauri Korhonen for collaboration. Data collection and preparation was supported by the Academy of Finland [BOREALITY, grant number 286390; and DIMEBO, grant number 3323004]; and by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme [grant agreement No 771049]. The content reflects only the authors’ view and the Agency is not responsible for any use that may be made of the information it contains.\r\n\r\nIf you use the dataset, please cite Forsström et al. [1,2]. \r\n\r\nA similar dataset for a boreal forest site in Finland is available from Forsström et al. [3]. Together, these datasets can be used to reproduce the results of Forsström et al. [1].\r\n\r\n[1] Forsström, P.R., Hovi, A., Juola, J., Rautiainen, M., 2023. Links between light availability and spectral properties of forest floor in European forests. Agricultural and Forest Meteorology. doi:10.1016/j.agrformet.2023.109481\r\n[2] Forsström, P.R, Hovi, A., Juola, J., Rautiainen, M., 2023. Dataset of tree canopy structure, understory reflectance spectra and fractional cover in hemiboreal and temperate forest areas in Estonia and the Czech Republic. Mendeley Data, V1. doi:10.17632/9dx32rszp8.1\r\n[3] Forsström, P.R., Juola, J., Hovi, A., Rautiainen, M., 2023. Dataset of understory reflectance spectra and fractional cover in a boreal forest area in Finland. Mendeley Data, V2. doi:10.17632/2g9nkcdj53.2"
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"en": "Abstract Aim This study aimed to evaluate the effects of using the teach-back method among women with limited maternal health literacy (LMHL) on maternal health literacy(MHL), postpartum health behaviours and maternal-infant health outcomes. Methods A randomized controlled study was conducted in the obstetrics department of Anhui Provincial Hospital, China. A total of 258 pregnant women with LMHL were recruited at the point of admission to the hospital for birth and randomly assigned to the control group (n = 130), where women received routine education sessions, and the teach-back group (n = 128), where women received routine education sessions plus a teach-back intervention. The two groups were assessed in terms of MHL before and after the intervention, breastfeeding execution, uptake of 42-day postpartum check-ups, complete uptake of one-time recommended vaccines, and physical health outcomes. Statistical tests were employed for data analysis. Results There was no significant difference between the two groups in terms of MHL and other social, demographic, and medical status at baseline. After the intervention, the teach-back group had a higher level of MHL (p < 0.001), better postpartum health behaviours in terms of exclusive breastfeeding within 24 hours postpartum (x2 = 22.853, p"
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"en": "Abstract Background A deep understanding of carcinogenesis at the DNA level underpins many advances in cancer prevention and treatment. Mutational signatures provide a breakthrough conceptualisation, as well as an analysis framework, that can be used to build such understanding. They capture somatic mutation patterns and at best identify their causes. Most studies in this context have focused on an inherently additive analysis, e.g. by non-negative matrix factorization, where the mutations within a cancer sample are explained by a linear combination of independent mutational signatures. However, other recent studies show that the mutational signatures exhibit non-additive interactions. Results We carefully analysed such additive model fits from the PCAWG study cataloguing mutational signatures as well as their activities across thousands of cancers. Our analysis identified systematic and non-random structure of residuals that is left unexplained by the additive model. We used hierarchical clustering to identify cancer subsets with similar residual profiles to show that both systematic mutation count overestimation and underestimation take place. We propose an extension to the additive mutational signature model—multiplicatively acting modulatory processes—and develop a maximum-likelihood framework to identify such modulatory mutational signatures. The augmented model is expressive enough to almost fully remove the observed systematic residual patterns. Conclusion We suggest the modulatory processes biologically relate to sample specific DNA repair propensities with cancer or tissue type specific profiles. Overall, our results identify an interesting direction where to expand signature analysis."
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